The identification of colorectal cancer (CRC) patients that will benefit from adjuvant chemotherapy after surgical resection poses a major unmet need in providing their safest and most effective care. The current practice results in under-treatment of high-risk stage II patients and overtreatment of low-risk stage III patients. The core obstacle is the lack of a definitive diagnostic biomarker(s) to identify cancers with a high probability of metastasis and corresponding poor clinical outcome. Translation of microarray-based profiles into clinical diagnostics is complicated by their complexity, as well as by logistical, cost and regulatory barriers. Pathological assessment of solid tumors typically Involves immunohistochemistry and other immunoassays to detect protein expression in formalin-fixed, paraffin-embedded (FFPE) tissue sections. Thus, there Is a gap between an emerging body of genomic information and diagnostic application. We propose to fill this gap by combining emerging genomic and proteomic technologies to identify and validate new molecular markers of colorectal cancer recurrence using FFPE tissue samples. We hypothesize that molecular encoding of a recurrence-prone phenotype in CRC is reflected by both transcriptomic and proteomic features. We will combine high-dimensional network analysis and new, targeted analysis platforms for specific transcripts and proteins to develop and test new biomarkers in archival FFPE specimens. We will test this hypothesis and develop these approaches according to the following specific aims: Aim 1: Develop our 34-gene nucleic acid-based colon cancer prognostic classifier for use in FFPE tissue samples and refine through a competitive evaluation of selected and published signature elements, using the novel nCounter multiplex expression analysis approach. We will use high-dimensional network models to predict protein biomarker candidates, which will be systematically evaluated with targeted proteomic analyses. Aim 2: Identify candidate protein biomarkers by targeted proteomics analysis. Aim 3: Test the protein-based and nucleic acid-based signature biomarkers in an independent set of archived colon cancer tissue samples annotated with patient outcomes.